One area in computer science that has been receiving a growing amount of attention is affective computing. This research area, which involves systems that are capable of recognizing and analyzing the expression of human emotions, has been a flourishing in recent years. Despite the growing interest, most affective computing systems are still research-grade endeavors that are typically not robust enough to handle the demands of real world applications.
One function some affective computing applications need is the ability to foretell a user's response to stimuli. Having this ability may enable the applications to offer an improved user experience. While currently there are some systems that learn to predict a user's response to stimuli, they are typically inadequate when it comes to real world applications. Existing systems are mostly trained on data collected in a controlled environment. In these laboratory-like settings, a small number of short experiments are conducted (typically less than an hour long), in which a user's responses are measured to a set of pre-selected stimuli, such as pictures, video scenes, or music. One potentially problematic issue that may arise with laboratory-collected data is the fact that in a laboratory, the user is often in very similar situations. In reality, a user's reaction to stimuli may vary dramatically depending on the situation the user is in, making the laboratory-collected data less useful. For example, a user's response while driving in busy traffic might be quite different from the user's response when relaxing at home, even if exposed to the same stimuli in both situations.
Another potentially problematic issue that may arise with laboratory-collected data is the fact that in a laboratory, the user is often in very similar situations. In reality, a user's reaction to stimuli may vary dramatically depending on the situation the user is in, making the laboratory-collected data less useful. For example, a user's response while driving in busy traffic might be quite different from the user's response when relaxing at home, even if exposed to the same stimuli in both situations.
With these many challenges and complications that are involved in the real world domain, a system designed to predict a user's response to stimuli in real world scenarios should take into account the added complexity intrinsic to this domain in order to achieve optimal results.